The state-of-the-art baseline method for combined clustering and classification is DMNN [1]}.
DMNN originally employs the \(k\) -means algorithm for clustering and a neural network based classifier.
In addition, we use 7 different base classifiers, implemented by sklearn library (BSD licenced): LR, Linear SVM, Linear Discriminant Analysis (LDA), single Perceptron, Random Forest (#trees = 10), \(k\)  Nearest Neighbors (\(k=5\) ), SGD classifier and Ridge classifier (Ridge regressor predicting over the range \([-1,1]\) ) as a baseline directly and with \(k\) -means (KM+X) in a `cluster-then-predict' approach.
CAC is also combined with DMNN where
\(k\) -means is replaced with CAC, and DMNN's underlying neural architecture is used. FNN is base DMNN but w/o any clustering. We implement DMNN ourselves in PyTorch framework. We also propose a Deep variant of CAC by incorporating CAC as the clustering module in DMNN.
